The present disclosure relates to an information processing device, an information processing method, and a program.
Recently, attention has been focused on a technique of mechanically extracting a feature quantity of a data group from any data group in which a feature is difficult to decide quantitatively. For example, a technique of automatically constructing an algorithm of receiving any music data and mechanically extracting a music genre to which the music data belongs has been known. Music genres such as jazz, classical, and pop are not quantitatively decided according to a type of musical instrument or a performance type. Thus, in general, it has previously been considered difficult to extract a music genre from music data when any music data is given.
However, actually, a feature of dividing music genres is potentially included in various information combinations such as a combination of musical intervals included in music data, a musical-interval combination method, a combination of types of musical instruments, and a structure of a melody line or a base line. Thus, a feature quantity extractor has been studied from a point of view of whether an algorithm of extracting the feature (hereinafter referred to as the feature quantity extractor) can be automatically constructed by machine learning. As one study result, for example, an automatic construction method of the feature quantity extractor based on a genetic algorithm is disclosed in Japanese Patent Application Laid-Open No. 2009-048266. The genetic algorithm considers selection, crossover, and mutation elements in a machine learning process following a biological evolution process.
It is possible to automatically construct the feature quantity extractor for extracting a music genre to which music data belongs from any music data using the algorithm of automatically constructing the feature quantity extractor disclosed in Japanese Patent Application Laid-Open No. 2009-048266. The algorithm of automatically constructing the feature quantity extractor disclosed in Japanese Patent Application Laid-Open No. 2009-048266 has very high versatility and is not limited to the music data, and can automatically construct the feature quantity extractor, which extracts a feature quantity of a data group from any data group. Thus, the algorithm of automatically constructing the feature quantity extractor disclosed in Japanese Patent Application Laid-Open No. 2009-048266 is expected to be applied to feature quantity analysis of artificial data such as music data or video data, feature quantity analysis of various observation quantities in nature, or the like.